SearcharxivSearch

arXiv subjects

David Daniels

Publications and source records attributed to David Daniels.

2 recordsLinked to original sources

Trust in Generative AI among students: An Exploratory Study

Generative artificial systems (GenAI) have experienced exponential growth in the past couple of years. These systems offer exciting capabilities, such as generating programs, that students can well utilize for their learning. Among many dimensions that might affect the effective adoption of GenAI, in this paper, we investigate students' \textit{trust}. Trust in GenAI influences the extent to which students adopt GenAI, in turn affecting their learning. In this study, we surveyed 253 students at two large universities to understand how much they trust \genai tools and their feedback on how GenAI impacts their performance in CS courses. Our results show that students have different levels of trust in GenAI. We also observe different levels of confidence and motivation, highlighting the need for further understanding of factors impacting trust.

cs.HC

Renewable levelized cost of energy available for export: An indicator for exploring global renewable energy trade potential

Renewable energy resources are widely available, yet they are unevenly distributed globally. In a renewable future, countries lacking high-quality renewable resources may choose to import energy from other countries. To assess the resource-dependent and techno-economic basis for global renewable energy trade and identify potential importers and exporters, this study introduces two new metrics: Renewable Levelized Cost of Energy available for Export (RLCOE_Ex) and Potential Energy Export Volume (PEEV). These metrics are computed based on regional resource potential, domestic energy demand and varying financial costs across countries, without the need for any energy system modeling. By applying these two metrics to 165 countries/regions, we identify countries with significant potential for exporting renewable energy (e.g., the US, China) and those that lack the domestic resources to satisfy demand (e.g., South Korea, Japan). The RLCOE_Ex and PEEV metrics are validated through a separate analysis, employing a comprehensive energy system model for each country/region.

physics.soc-ph